Document Type: Research Paper
Department of Geology, Faculty of Sciences, Tabriz University , Tabriz, Iran
Department of Geology, Faculty of Sciences, Ferdowsi University , Mashhad, Iran
Research Institute of Petroleum Industries (RIPI), Tehran, Iran
Department of Geology, Faculty of Sciences, Ferdowsi University of Mashhad, Iran
National Iranian South Oil Company (NISOC), Geophysics Department, Ahvaz, Iran
3D seismic attributes and well logs were used to estimated porosity and water saturation in the Asmari Formation in the Dezful Embayment, SW Iran. For this purpose, at first, the 3D seismic volume was inverted base on the model, to obtain acoustic impedance cube. Afterward, the impedance and other attributes extracted from seismic volume were analyzed by multiple attribute regression transform and neural networks to predict porosity and water saturation between wells. Then linear or non-linear combinations of attributes performed for porosity and water saturation prediction. The result shows that the match between the actual and predicted porosity and water saturation improved; using only a single attribute to the derived multi attribute transforms and neural networks model. Based on the results of neural networks, the highest cross-correlation was observed between seismic attributes and the observed target logs between seven wells in the study area. Based on our study, the cross-correlation between actual and predicted porosity and water saturation increased and reached 93% and 90% respectively in the case of using probabilistic neural networks (PNN). Finally, according to the cross-validation results, PNN neural networks are used for porosity and water saturation prediction.